Random Number Generator

About This Tool

Purpose & User Intent:\nThe tool solves the need to generate numeric samples on demand without coding. It targets developers, QA engineers, educators, data scientists, and researchers who require repeatable sequences for tests, simulations, or demonstrations. When a seed is provided, outputs are deterministic, enabling reproducibility across environments. It also supports quick exploratory sampling where exact distributions are not pre-coded in a project.\n\nCore Logic & Features:\nRequired features include input validation for min <= max, count > 0, and type coercion to numeric values. The core capability is a seedable pseudo-random number generator with two distribution modes: uniform over [min, max] and normal with configurable mean and standard deviation or bounds. Optional features include adjustable decimal precision, bulk generation, and export options. The tool runs entirely in the client, with no server dependency.\n\nInputs & Outputs:\nInputs: min (number), max (number), count (integer), distribution (string: 'uniform','normal'), seed (string|number, optional), precision (integer, optional). Outputs: an array of numbers of length count; integers when using integer ranges or uniform integer mode, otherwise floating numbers with fixed decimals per precision. The tool also returns the used seed for traceability.\n\nAlgorithms & Calculations:\nUniform: sample = min + (max - min) * u where u in [0,1) from PRNG. Normal: apply Box–Muller to obtain z, then transform to target mean and stddev or bounds; apply clipping if required. Seed initializes RNG; results are deterministic for the same inputs.\n\nError & Edge Cases:\nInvalid ranges (min>max), non-positive count, or non-numeric inputs produce errors or fallback defaults. Missing inputs use sensible defaults (min=0, max=1, count=1, distribution='uniform'). Large counts may impact performance; non-finite values are sanitized.\n\nIndustry/Region & Localization:\nNumeric outputs use a dot as decimal separator; locale-specific formatting is not applied by default. Seeding enables reproducibility across platforms.\n\nAssumptions & Exclusions:\nAssumes a standard PRNG; cryptographic randomness is out of scope; UI styling, or server-based randomness is not included.

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